One interesting technique I didn't cover was using Matrix Factorization methods to reduce the dimensionality of the data before calculating the related artists. This kind of analysis can generate matches that are impossible to find with the techniques in my original post.

This post is a step by step guide on how to calculate related artists using a couple of different matrix factorization algorithms. The code is written in Python using Pandas and SciPy to do the calculations and D3.js to interactively visualize the results.

Premise[This post is an introduction to reinforcement learning and it is meant to be the starting point for a reader who already has some machine learning background and is confident with a little bit of math and Python. When I study a new algorithm I always want to understand the underlying mechanisms. In this sense it is always useful to implement the algorithm from scratch using a programming language. I followed this approach in this post which can be long to read but worthy. I am not a native English speaker so if you spot some mistakes that make a sentence puzzling please report it in the comments below and I will fix it]

Chatbots are all the rage at the moment, with some predicting that they will be bigger than mobile apps. The main idea of chatbots is that instead of having to dig through awkward mobile menus and learn UIs, you'll simply have a conversation with a bot through a familiar instant messaging interface. If you want to order a Pizza, you start a conversation with the Domino's Pizza bot and have the same conversation with it that you might have with a human.

There are a few different platforms that allow you to build your own chatbot. One of these, which is arguably the simplest to use and is also growing steadily in popularity, is Telegram.

In this tutorial, we'll walk through building a simple Telegram Bot using Python. At first, our bot will simply echo back any message we send it, but then we'll extend it to add a database and persist information across chat sessions.